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- W181842374 abstract "This paper proposes a probabilistic logic abstraction for modelling tf -boosting approaches to anchor text retrieval, adapted for the task of page-search in books. The underlying idea is to view the backof-book index (BoBI) as a list of anchors pointing to pages in the book. First, we model the direct application of hypertext-based tf boosting to books and show that this naive method of propagating anchor-text from the BoBI does not deliver the desired tf -boosting effect. To address this, we then propose a revised anchor-text retrieval model based on a novel voter approach. In this approach, each page of the book, where a given term occurs, acts as a virtual voter to the pages referenced by the BoBI for that term. The tf boosting effect is achieved by propagating term weights from the voter pages to the pages in the BoBI. We use probabilistic Datalog for the high-level abstract modelling of retrieval strategies, which allows for the evolution and transfer of successful techniques from one domain, such as anchor-text retrieval in Web IR, to a similar domain, such as book search." @default.
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- W181842374 date "2008-01-01" @default.
- W181842374 modified "2023-09-26" @default.
- W181842374 title "Modelling Anchor Text Retrieval in Book Search based on Back-of-Book Index" @default.
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